agent didn't along with it. The second property is scalability. Most harnesses will run one process per agent, which means that the agent is tied to the machine running it. When the log is the state, you flip that model. One process can now advance thousands of agents. Each of them can reconstruct their state from the log on each turn, and they don't need to be tied to any single machine or worker. This makes failover trivial, and it also makes scaling just a matter of adding more workers. There's no sticky sessions, there's no state migration, and there's no coordination overhead. Forking becomes a whole lot more natural, too. Instead of having to force like one linear path, you can easily branch the log. One branch can run on Claude, another branch can run on GPT, another can run on your favorite open-source model. Each of the branches can store some of the history up until the fork point, and then they can explore different strategies.